This file describes the organisation and content of the data set:

"Results from Fitting 200 Different Models to 10,150 Flow-Occupancy Empirical Fundamental Diagrams of Road Traffic"


File Name Formats:
------------------

The data are organised according to the following file name formats:

Loop.Detector.And.Empirical.Fundamental.Diagram.Properties.txt
Fit.Results.For.<COUNTRY>.<CITY>.<DETECTOR_ID>.txt

where <COUNTRY> is a country name, <CITY> is a city name, and <DETECTOR_ID> is a loop detector ID.


Properties Of The Loop Detectors And Their Corresponding Empirical Fundamental Diagrams:
----------------------------------------------------------------------------------------

The properties of the loop detectors (including the roads on which they are located) and their corresponding empirical fundamental diagrams are stored in the ASCII
text file:

Loop.Detector.And.Empirical.Fundamental.Diagram.Properties.txt

This file has 10,150 data rows, and each data row corresponds to a unique loop detector. The file has the following data columns:

Column 1 - STRING VECTOR - Name of the country in which the loop detector is located.
Column 2 - STRING VECTOR - Name of the city in which the loop detector is located.
Column 3 - STRING VECTOR - Loop detector ID made up exclusively from characters in the set {'_', '-', '+', '0', ..., '9', 'a', ..., 'z', 'A', ..., 'Z'}. The values
                           in this column are not unique, and so to identify a loop detector uniquely, the information in this column must be combined with the
                           information given in column 2.
Column 4 - STRING VECTOR - Classification of the road on which the loop detector is located (from Open Street Maps). The values in this column are from the set
                           {'living_street', 'motorway', 'motorway_link', 'primary', 'primary_link', 'residential', 'secondary', 'secondary_link', 'service',
                           'tertiary', 'tertiary_link', 'trunk', 'trunk_link', 'unclassified'}.
Column 5 - FLOAT64 VECTOR - Speed limit (km/h) of the road on which the loop detector is located (from Open Street Maps). Good values in this column are positive,
                            while bad values are -1.0.
Column 6 - FLOAT64 VECTOR - Length (km) of the road on which the loop detector is located. The values in this column are positive.
Column 7 - FLOAT64 VECTOR - Loop detector location as a ratio of the distance from the downstream intersection to the length of the road on which the loop detector
                            is located. The values in this column are in the range 0.0 to 1.0 inclusive.
Column 8 - FLOAT64 VECTOR - Loop detector location on a road specified as the distance (km) of the loop detector from the downstream intersection. The values in
                            this column are non-negative.
Column 9 - INT32 VECTOR - Number of flow-occupancy measurement pairs (not flagged as errors and with positive occupancy) in the corresponding empirical fundamental
                          diagram. The values in this column are positive.
Column 10 - FLOAT64 VECTOR - Aggregation time-interval (min) for each flow-occupancy measurement pair. The values in this column are positive.
Column 11 - FLOAT64 VECTOR - Minimum (good) occupancy measurement. The values in this column are positive and less than 1.0.
Column 12 - FLOAT64 VECTOR - Maximum (good) occupancy measurement. The values in this column are positive and less than or equal to 1.0.
Column 13 - FLOAT64 VECTOR - Minimum (good) flow measurement (veh/h). The values in this column are non-negative.
Column 14 - FLOAT64 VECTOR - Maximum (good) flow measurement (veh/h). The values in this column are positive.
Column 15 - FLOAT64 VECTOR - Maximum useful occupancy measurement. The values in this column are positive and less than or equal to 1.0.


Fit Results:
------------

The results of the model fits to the flow-occupancy empirical fundamental diagrams are stored in ASCII text files with names of the form:

Fit.Results.For.<COUNTRY>.<CITY>.<DETECTOR_ID>.txt

Each file contains the results from fitting 200 different models to a single empirical fundamental diagram corresponding to a unique loop detector. The strings
<COUNTRY>, <CITY>, and <DETECTOR_ID> indicate the country, city, and ID, respectively, of the corresponding loop detector.

There are 10,150 such files. Each file has 200 data rows, and each data row corresponds to a different model. The order of the models in each file is the same. The
data columns in each file are as follows:

Column 1 - STRING VECTOR - Abbreviated name of the functional form component in the fitted model (see the related publications for details).
Column 2 - STRING VECTOR - Abbreviated name of the noise component in the fitted model (see the related publications for details).
Column 3 - FLOAT64 VECTOR - Number of free parameters for "mu" in the fitted model. If a model fit was successful, then the value in this column is positive,
                            otherwise it is -1.0.
Column 4 - FLOAT64 VECTOR - Number of free parameters for "sigma" in the fitted model. If a model fit was successful, then the value in this column is positive,
                            otherwise it is -1.0.
Column 5 - FLOAT64 VECTOR - Number of free parameters for "nu" in the fitted model. If a model fit was successful, then the value in this column is non-negative,
                            otherwise it is -1.0.
Column 6 - FLOAT64 VECTOR - Number of free parameters for "tau" in the fitted model. If a model fit was successful, then the value in this column is non-negative,
                            otherwise it is -1.0.
Column 7 - FLOAT64 VECTOR - Value of -2 ln L for the fitted model. If a model fit was successful, then the value in this column is any number, otherwise it is -1.0.
Column 8 - FLOAT64 VECTOR - Value of the Akaike information criterion (AIC) for the fitted model. If a model fit was successful, then the value in this column is
                            any number, otherwise it is -1.0.
Column 9 - FLOAT64 VECTOR - Probability according to the AIC that the fitted model is the best (most parsimonious) model for the data. The values in this column are
                            in the range 0.0 to 1.0 inclusive.
Column 10 - FLOAT64 VECTOR - Value of the Bayesian information criterion (BIC) for the fitted model. If a model fit was successful, then the value in this column is
                             any number, otherwise it is -1.0.
Column 11 - FLOAT64 VECTOR - Probability according to the BIC that the fitted model is the best (most parsimonious) model for the data. The values in this column are
                             in the range 0.0 to 1.0 inclusive.
